# How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation

arXiv: https://arxiv.org/abs/2201.04672 · HTML: https://mlsanity.com/paper/2201.04672/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 12 Jan 2022.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Invariant and Equivariant Graph Networks](https://mlsanity.com/paper/1812.09902/) (2018)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [Universal Invariant and Equivariant Graph Neural Networks](https://mlsanity.com/paper/1905.04943/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [On the Universality of Invariant Networks](https://mlsanity.com/paper/1901.09342/) (2019)
- [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://mlsanity.com/paper/1802.00910/) (2018)
- [On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs](https://mlsanity.com/paper/2107.01495/) (2021)
- [BioSentVec: creating sentence embeddings for biomedical texts](https://mlsanity.com/paper/1810.09302/) (2018)
- [Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID](https://mlsanity.com/paper/2104.09632/) (2021)
- [Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders](https://mlsanity.com/paper/1911.05465/) (2019)
- [A Graph-based Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/2101.11873/) (2021)
- [IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles](https://mlsanity.com/paper/2007.12603/) (2020)
- [Evaluation of Unsupervised Entity and Event Salience Estimation](https://mlsanity.com/paper/2104.06924/) (2021)

## Built on by (most cited first, 7 of 7)

- [ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval](https://mlsanity.com/paper/2305.10703/) (2023)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models](https://mlsanity.com/paper/2311.00287/) (2023)
- [Neighborhood-Regularized Self-Training for Learning with Few Labels](https://mlsanity.com/paper/2301.03726/) (2023)
- [PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis](https://mlsanity.com/paper/2305.14376/) (2023)
- [Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training](https://mlsanity.com/paper/2306.07193/) (2023)
- [Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting](https://mlsanity.com/paper/2310.18804/) (2023)

## Abstract (arXiv)

Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured texts represented as concept maps, GNNs can be exploited for tasks like document retrieval. Intrigued by how can GNNs help document retrieval, we conduct an empirical study on a large-scale multi-discipline dataset CORD-19. Results show that instead of the complex structure-oriented GNNs such as GINs and GATs, our proposed semantics-oriented graph functions achieve better and more stable performance based on the BM25 retrieved candidates. Our insights in this case study can serve as a guideline for future work to develop effective GNNs with appropriate semantics-oriented inductive biases for textual reasoning tasks like document retrieval and classification. All code for this case study is available at https://github.com/HennyJie/GNN-DocRetrieval.